Scalable bottom-up subspace clustering using FP-trees for high dimensional data

Doan, Minh Tuan, Qi, Jianzhong, Rajasegarar, Sutharshan and Leckie, Christopher 2018, Scalable bottom-up subspace clustering using FP-trees for high dimensional data, in Big Data : Proceedings of the 2018 IEEE International Conference on Big Data, Institute of Electrical and Electronics Engineers, Piscataway, N.J., pp. 106-111, doi: 10.1109/BigData.2018.8622122.

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Title Scalable bottom-up subspace clustering using FP-trees for high dimensional data
Author(s) Doan, Minh Tuan
Qi, Jianzhong
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Leckie, Christopher
Conference name IEEE Computer Society. Conference (2018 : Seattle, Wash.)
Conference location Seattle, Wash.
Conference dates 2018/12/10 - 2018/12/13
Title of proceedings Big Data : Proceedings of the 2018 IEEE International Conference on Big Data
Editor(s) Abe, Naoki
Liu, Huan
Pu, Calton
Hu, Xiaohua
Ahmed, Nesreen
Qiao, Mu
Song, Yang
Kossman, Donald
Liu, Bing
Lee, Kisung
Tang, Jiliang
He, Jingrui
Saltz, Jeffrey
Publication date 2018
Series IEEE Computer Society Conference
Start page 106
End page 111
Total pages 6
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Keyword(s) Subspace clustering
Bottom-up clustering
Frequent pattern mining
Bioinformatics
Internet of Things
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
ISBN 9781538650356
Language eng
DOI 10.1109/BigData.2018.8622122
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30120925

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